Free ML‑in‑Finance code

Machine learning in finance got a new free on-ramp: Quant Science is pointing readers to Python code for *Machine Learning in Finance: From Theory to Practice*, a textbook-backed repository of notebooks and datasets. (github.com) (quantscience.io) In quantitative finance, machine learning means training statistical models on market or company data to classify, forecast, or choose actions, much like pattern-recognition software learns from past examples. Springer’s description of the 2020 book says it covers supervised learning and reinforcement learning for financial modeling and decision-making. (link.springer.com) The code lives in the GitHub repository `mfrdixon/ML_Finance_Codes`, which GitHub lists at about 2,600 stars and roughly 630 forks as of April 19, 2026. The repository says it is the official source code for the book by Matthew Dixon, Igor Halperin, and Paul Bilokon. (github.com 1) (github.com 2) The repository is organized by chapter, with folders for probabilistic modeling, neural networks, sequence modeling, reinforcement learning, and inverse reinforcement learning. It also includes setup files, a Google Colab notebook, and sample data for running the examples. (github.com 1) (github.com 2) That matters because finance students and self-taught traders often find the gap between a textbook equation and a working notebook wider than it looks. The repository’s stated aim is reproducibility: matching package versions, documenting setup, and letting readers rerun the same workflows on Mac, Windows 10, and Linux. (github.com 1) (github.com 2) Quant Science has built its brand around that hands-on pitch, publishing tutorials that pair finance concepts with downloadable Python code. Its GitHub profile lists three public repositories, and its newsletter repeatedly markets “get the code” alongside lessons on portfolios, Polars, and backtesting. (github.com) (quantscience.io) The book itself was published by Springer in 2020, and a later review in *Mathematics and Financial Economics* said it tried to close the gap between quantitative finance and statistical machine learning. The GitHub repository’s latest visible commits are older, but the codebase still spans 249 commits and 11 chapter directories, which makes it a substantial teaching archive rather than a single demo notebook. (link.springer.com) (link.springer.com) (github.com) The practical catch is that free code is not the same as production-ready trading software. The repository’s license is MIT, and its disclaimer says the software is provided “as is,” which is standard language for educational code but important in a field where bugs can turn into losses. (github.com) So the release is less about a new model than about access: a reader can move from a finance chapter to a runnable notebook without rebuilding the examples from scratch. For anyone trying to learn how machine learning gets applied to markets, that shortens the distance between theory and code. (github.com) (link.springer.com)